Flow cytometry is used to rapidly gather large quantities of data on cell type and function. The manual process of classifying hundreds of thousands of cells forms a bottleneck in diagnostics, high-throughput screening, clinical trials, and large-scale research experiments. The process currently requires a trained technician to identify populations on a digital graph of the data by manually drawing regions. As the complexity of the data increases, this gating task becomes more lengthy and laborious, and it is increasingly clear that minimizing human processing is essential to increasing both throughput and consistency. In clinical tests and diagnostic environments, automated gating would eliminate a complex set of human instructions and decisions in the Standard Operating Procedure (SOP), thereby reducing error and speeding results to the doctor. In many cases, the software will be able to recognize the need for additional tests before the doctor has an opportunity to look at the first report. Currently no software is available to perform complex multi-parameter analyses in an automated and rigorously validated manner. FlowDx will fill an important gap in the evolution of the technology and pave the way for ever larger phenotypic studies and for the translation of this research process to a clinical environment. Specific Aims 1) Fully define the experimental protocol, whereby a researcher can compare two or more classifications of identical data sets to study the differences, biases and effectiveness of human and algorithmic classifiers. 2) Describe and evaluate metrics that compare the performance of classification algorithms. 3) Conduct analytical experiments on our identified use cases, illustrating the potential of this technique to affect clinical analysis. 4) Iteratively implement the tools to automate these experiments, improve the experimental capabilities, and collaborate in new use cases. These aims will be satisfied while maintaining quantitative standards of software quality, establishing measurements in system uptime, throughput and robustness to set the baseline for subsequent iterations. PUBLIC HEALTH RELEVANCE: FlowDx, a Clinical Cytometry Analysis Software Project is designed to create a new, more efficient, and more effective way of analyzing cells for the presence of cancer, HIV/ AIDS, and other diseases, using a fully automated software system. Using Magnetic Gating, Probability Clustering, Subtractive Cluster Analysis, Artificial Neural Networks, and Support Vector Machines (SVM), Tree Star software will analyze the cell samples from patients at a much faster rate and with fewer false positives and negatives than the manual method now in use. The FlowDx Project 1) Fits the "translational medicine" model of the NIH Roadmap 2) Reduces error in the diagnosis of cancer and other diseases 3) Speeds results to physicians. Patients learn the outcome more quickly. Therapeutic intervention is faster. 4) Accommodates large-scale research by allowing greater volumes of complex data to be much more quickly examined, compared, and quantified 5) Reduces the expense of cell analysis by as much as 50% 6) Conforms to 21CFR Part 11 guidance